System and method for scalable real-time micro-object position control with the aid of a digital computer

ABSTRACT

The system and method described below allow for real-time control over positioning of a micro-object. A movement of at least one micro-object suspended in a medium can be induced by a generation of one or more forces by electrodes proximate to the micro-object. Prior to inducing the movement, a simulation is used to develop a model describing a parameter of an interaction between each of the electrodes and the micro-object. A function describing the forces generated by an electrode and an extent of the movement induced due to the forces is generated using the model. The function is used to design closed loop policy control scheme for moving the micro-object towards a desired position. The position of the micro-object is tracked and taken into account when generating control signals in the scheme.

CROSS-REFERENCE TO RELATED APPLICATION

This non-provisional patent application claims priority under 35 U.S.C.§ 119(e) to U.S. Provisional Patent application, Ser. No. 62/396,741,filed Sep. 19, 2016, the disclosure of which is incorporated byreference.

This invention was made with government support under Contract No.FA8650-15-C-7544 awarded by the Defense Advanced Research ProjectsAgency. The government has certain rights in the invention.

FIELD

This application relates in general to micro-assembly control, and inparticular, to a system and method for scalable real-time micro-objectposition control with the aid of a digital computer.

BACKGROUND

The degree to which control can be exercised over assembly ofmicro-objects, objects whose dimensions measure in microns, can make asignificant difference in a number of technologies. For example,manufacturing of reconfigurable electrical circuits can be improved bybeing able to accurately control positioning of micro-objects such ascapacitors and resistors to manufacture a circuit with a desiredbehavior. Similarly, production of photovoltaic solar cell arrays canbenefit from being able to put photovoltaic cells with certain qualitiesinto particular positions on the arrays. Such cells can be too small toallow for a desired placement of the cells via human or roboticmanipulation, and another kind of transportation mechanism is needed.Micro-assembly of particles can also be used to engineer themicrostructure of materials. Biological cells being assembled intotissue need to be positioned and oriented. Achieving rapid directedassembly of micro objects to desired positions and with desiredorientations is generally needed. Many other technological fields existwhere increasing control over assembly of micro-objects can providesignificant benefits.

Existing methods do not allow for control of movement of micro-objectswith the required degree of precision. For example, uncontrolledmechanical agitation is typically used for directed particle assembly.However, this technique fails to achieve the near 100% yield necessaryfor certain industrial applications, such as electronics assembly.

Previous work has also attempted to use an electric field to directmovement of micro-objects. For example, Edwards et al., “ControllingColloid Particles With An Electric Field,” discusses control of colloidparticle ensembles (1 μm to 3 μm in diameter) and individual colloidsusing inhomogeneous electric fields. In particular, the individualcolloid particles and the ensembles of the colloid particles suspendedin water and sodium hydroxide solutions are manipulated throughelectrophoresis and electroosmosis using two parallel electrodes. Themanipulation is done with the assumption that the electric field iscompletely dictated by the two parallel electrodes and is not disturbedby presence of the particles. Optical-based feedback control is used tomonitor assembly and disassembly of colloid crystals. The feedbackcontrol focuses on groups of particles, thus not being able to put aparticular particle in a desired position. However, the relative size ofthe colloids to the electrodes employed to generate the field, themedium in which the particles were immersed, and the resultingmathematical models, do not allow the described techniques to be used incertain industrial applications. In particular, the described techniquesare not suitable for assembling micro-objects even slightly larger thanthose discussed in the Edwards paper. Further, the control schemes usedinvolve high frequency signals (MHz), which further limits theapplicability of such techniques.

Other works, such as Xue et al., “Optimal design of a colloidalself-assembly process.” IEEE Transactions on Control Systems Technology,22(5):1956-1963, September 2014, and Xue et al., “Mdp based optimalcontrol for a colloidal self-assembly system” American ControlConference (ACC), 2013, pages 3397-3402, June 2013, discuss using aMarkov-Decision Process optimal control policy to control a stochasticcolloidal assembly and drive the system to a desired high-crystallinitystate. Actuator-parametrized Langevin equations are used to describe thesystem dynamics. However, the described approach does not allow directmanipulation of individual particles. As individual particle control iseven more difficult when assembling electrical circuits, whether thedescribed techniques can be used for electrical circuit assembly remainsunclear. In addition, the particle size (≈3 μm in diameter) described inthese works poses little disturbance to the electric field that iscompletely shaped by an actuation potentials. Moreover, the time scalefor achieving the desired state would make the goal of high throughputchallenging to achieve when the described techniques are applied.

Still other works, such as Qian et al., “On-demand and locationselective particle assembly via electrophoretic deposition forfabricating structures with particle-to-particle precision,” Langmuir,31(12):3563-3568, 2015, PMID: 25314133, have demonstrated singleparticle precision and location selective particle deposition, withelectrophoretic forces being the primary drive for particle (2 μmpolystyrene beads) manipulation. Shaping the energy landscape bybuilding large energy wells closed to the desired location of thenano-particles was the chosen approach for controlling the formation ofnano-structures. Although an important part in fabricating structures,the described techniques have limited industrial applications asparticle deposition would not suffice for achieving potentially complexstructures that may appear in electrical circuits. In addition thisapproach is not compatible with a subsequent transfer of the assembly toa final substrate, which is generally needed for the assembly to be usedin most applications.

Still other techniques for control of assembly of microscopic particleshave been proposed. For example, R. Probst et al. “Flow control of smallobjects on chip: Manipulating live cells, quantum dots, and nanowires,”IEEE Control Systems, 32(2):26-53, April 2012, describes using electricfield induced electroosmotic flow actuation to precisely manipulatecells, quantum dots and nano-wires. Linear models in the electrodepotentials for the particle motion are obtained and are used to designcontrol schemes using least square methods. In addition, the particles'effect on the electric field distribution is negligible. Thus, thismodel is inapplicable where the linearity no longer holds and electrodepotentials are where the electric field is affected by the particleposition. Another approach is described in U.S. Pat. Nos. 7,651,598 and8,110,083, issued to Shapiro et al. focuses on fluid flow actuation tocontrol fluid flow fields to move particles. The main mechanism forparticle transport described by Shapiro et al. is electroosmoisis. Thismechanism results in a linear control scheme in the voltages applied tothe electrodes. In addition, the Shapiro approach for generating controlcommands involves inverting a matrix, an operation which does not scalewith the size of electrodes and may not be practical when a large numberof electrodes is involved.

Still other techniques have been proposed. For example, since bothelectrophoretic forces as well as fluid motions of electro-osmotic flowscan be used to drive particles, water based solution in which particlesare immersed is a popular choice that have been explored in works suchas Tolley et al., “Dynamically programmable fluidic assembly.” AppliedPhysics Letters, 93(25), 2008. However, these models are applicable toparticles that are spherical, and may not be suitable for controllingparticles of other shapes and thus have limited industrialapplicability.

Accordingly, there is a need for a way to control movement ofmicro-objects with a degree of precision and scalability sufficient forindustrial applications.

SUMMARY

The system and method described below allow for real-time control overpositioning of a micro-object. A movement of at least one micro-objectsuspended in a fluid, such as a dielectric medium, can be induced by ageneration of a group of forces, such as a voltage induced electrostaticforce through electrodes proximate to the micro-object. Prior toinducing the movement, a simulation is used to develop a modeldescribing a parameter of an interaction between the electrodes and themicro-object (such as the capacitance). A base function describing avoltage generated by an electrode and an extent of the movement induceddue to the voltage is generated using the model. The base function isused to design a closed loop policy control scheme for moving themicro-object towards a desired position. The position of themicro-object is tracked and taken into account when generating controlsignals in the scheme.

Due to performing the modeling and base function derivation prior togenerating the control scheme, the computational effort to generate thescheme in real-time decreases to make the real-time control of themicro-object position scalable for industrial applications. Thedescribed system and method can also be scaled to simultaneously controlpositions of multiple micro-objects.

In one embodiment, a system and method for scalable real-timemicro-object position control with the aid of a digital computer areprovided. One or more parameters of a system for positioning of at leastone micro-object are obtained, the system comprising a plurality ofelectrodes, the electrodes configured to induce a movement of themicro-object when that micro-object is suspended in a fluid proximate tothe electrodes upon a generation of one or more forces by theelectrodes. A parameter of an interaction between each of the electrodesand the micro-object is modeled as a function of a distance between thatelectrode and the micro-object. A position of the micro-object withinthe fluid is obtained. A control signal is generated for each of theelectrodes, the control signal comprising a command to generate one ormore of the forces for the predefined period of time by that electrode,using the obtained position, the interaction parameter function, and adesired location of the micro-object.

Still other embodiments of the present invention will become readilyapparent to those skilled in the art from the following detaileddescription, wherein is described embodiments of the invention by way ofillustrating the best mode contemplated for carrying out the invention.As will be realized, the invention is capable of other and differentembodiments and its several details are capable of modifications invarious obvious respects, all without departing from the spirit and thescope of the present invention. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a system for scalable real-timemicro-object position control with the aid of a digital computer.

FIG. 2 is a diagram showing a spiral electrode unit that includes fourspiral electrodes of FIG. 1, in accordance with one embodiment.

FIG. 3 is a diagram showing, by way of example, positions of twomicro-objects on the spiral unit of FIG. 2.

FIG. 4 is a diagram showing a representation of four electrodes and themicro-objects as forming a capacitance electrical circuit.

FIG. 5 is a diagram depicting, by way of example, C(x,y), thecapacitance between the micro-object and the electrodes as a function ofmicro-object horizontal position, with y constant, where the simulatednumerical values given were fitted on a continuously differentiablefunction.

FIG. 6 is diagram showing, by way of example, results of a templatematching algorithm.

FIGS. 7A-7D are diagrams showing, by way of example, the resultingelectrode voltage patterns as function of the micro-object position fromsolving a sequence of optimization problems of equation (8).

FIG. 8 is a diagram showing, by way of example, a base functiondescribing the dependence of a voltage generated by one electrode andthe movement of a micro-object induced by the voltage field generated bythat electrode.

FIG. 9 is a diagram showing, by way of example, an impulse-basedreal-time control scheme.

FIGS. 10A-10D are diagrams showing, by way of example, a control schemefor using the control unit of FIG. 2 to move a micro-object 1000 μm.

FIG. 11 is a diagram showing, by way of example, a result of applicationof the control scheme of FIG. 10.

FIG. 12 is a flow diagram showing a method for scalable real-timemicro-object position control with the aid of a digital computer.

FIG. 13 is a flow diagram describing a routine for generating thecapacitance-based model for use in the method of FIG. 12 in accordancewith one embodiment.

FIG. 14 is a flow diagram showing a routine for deriving a base functionfor control signal generation for use in the method of FIG. 12 inaccordance with one embodiment.

FIG. 15 is a flow diagram showing a routine for tracking a position of amicro-object for use in the method of FIG. 12 in accordance with oneembodiment.

DETAILED DESCRIPTION

Real-time control over movements of a micro-object can be accomplishedby combining real-time micro-object position tracking and use of alumped, capacitance-based motion model to predict voltages generated byelectrodes proximate to the micro-object necessary to move themicro-object towards a desired position. The tracking provides real-timefeedback that can be used to correct the position of the micro-object.While the system and method below are described with reference tocontrolling position of a single micro-object, the system and method canalso be used to simultaneously control the positions of multiplemicro-objects. Further, while the interaction between the electrodes andthe micro-object below is described in terms of electric forces, thesystem and method described below can use other forces besides electricforces, such as magnetic forces, in which case inductances would be anappropriate parameter to simulate. Chemical potentials and other drivingforces with predictable spatial dependencies can also be used.

FIG. 1 is a block diagram showing a system 10 for scalable real-timemicro-object position control with the aid of a digital computer. Thesystem 10 includes a dielectric fluid 11 in which are immersed one ormore micro-objects 12. The dielectric fluid 11 can be contained by anenclosure 13 on the sides and a layer of film 14 on the bottom. In oneembodiment, the dielectric fluid 11 can be Isopar® M, manufactured byExxonMobil Chemical Company of Spring, Tex. doped withdi-2-ethylhexylsulfosuccinate (AOT) charge director molecules, thoughother dielectric fluids 11 are also possible. In one embodiment, thefilm 14 can be a 50 μm thick perfluroalkoxy (PFA) film, though otherkinds of film 14 of other thicknesses are possible. A micro-object 12can be any object whose dimensions measure in microns. In oneembodiment, the micro-objects 12 can be non-spherical silicon chipletsof 300 μm×200 μm×50 μm dimensions; such chiplets are of a rectangularshape. In another application, for engineered microstructure for opticalmetamaterials, the micro-objects could be particles 0.2 um in diameter.Unless otherwise indicated, these 200 um silicon chiplet ofmicro-objects 12 were used to obtain the experimental data below. Otherkinds of micro-objects 12 of varying dimensions and shapes, includingthose of a spherical shape, can also be controlled using the system 10.

The film 14 is laminated on a plurality of electrodes 15A-15D thatgenerate voltages when electric potentials are applied to theseelectrodes 15A-15D. In one embodiment, the electrodes 15A-15D are spiralelectrodes and are arranged into a spiral electrode unit. FIG. 2 is adiagram showing a spiral electrode unit 40 that includes four spiralelectrodes 15A-15D of FIG. 1, in accordance with one embodiment.Electrodes 15-A-15D can each be independently driven with differentvoltages having different patterns, amplitudes, frequency and phase. Ascan be seen with reference to FIG. 2, the sequence of electrodes 15A-15Drepeats periodically starting from the outside and moving towards thecenter of the unit 40. The radial spacing between electrodes 15A-15D is100 μm. In one embodiment, the width of the electrodes is 100 μm, andthe radial distance between the electrodes 15A-15D can be 100 μm; in afurther embodiment, other radial distances and widths of the electrodes15A-15D are possible. The electrostatic potentials created by theseelectrodes 15, while limited in spatial freedom, are rich in temporalfreedom and the spiral arrangement of the electrodes can be used toinduce a radial motion of the micro-objects 12 (away or towards thecenter of the spiral).

As described further below, a generation of voltages by the electrodes15A-15D can induce a change in positions of micro-objects 12 by changingthe force between the micro-object and the electrode. FIG. 3 is adiagram showing, by way of example, positions of two hexagonalmicro-objects 12 on the spiral unit 40 of FIG. 2. As further describedbelow, the movement of the micro-objects 12 in the relation to theelectrodes 15A-15D in the response to the generation of the electricfield by the electrodes 15A-15D can be tracked. Coordinates 16 of themicro-objects 12, such as the micro-object 12 circled 17 in FIG. 3, canbe calculated based on a micro-object's 12 position in relation to theelectrodes 15, and the magnitude of the micro-object's 12 movement canbe calculated based on a difference in the coordinates 16 before andafter a generation of an electric fields. The coordinates correspond tothe center of the identified micro-object 12.

While electrodes 15A-15D described with reference to FIGS. 2 and 3 arespiral, the electrodes 15A-15D can be of other shapes and combined intoother configurations. The number of the electrodes 15A-15D in the arraycan be a number other than four. For example, in a further embodiment,non-spiral electrodes 15A-15D can be arranged in a two dimensional arraythat is used to control movements of electrodes. In a still furtherembodiment, the electrodes could also be arranged in a two dimensionalgrid pattern.

Depending on the system 10, varying numbers of additional electrodes15A-15D could be included into the system 10, and thus varying forcecontrol patterns could be applied, as each of these electrodes could beindependently controlled. So while the above spiral electrodes 15A-15Dhave 4 separate control signals, a generalized array of many electrodes,the numbers of electrodes reaching millions in one embodiment, similarto a display backplane, could be used to generate pixelated electrodepatterns; approximating the spirals or other patterns. US PatentApplication Publication No. 20160351584, entitled “Active MatrixBackplane Formed Using Thin Film Optocouplers,” published Dec. 1, 2016,the disclosure of which is incorporated by reference, describes a devicefor generating these force patterns with electrode arrays. U.S. Pat. No.7,332,361 “Xerographic Micro-assembler,” issued Feb. 19, 2008, to Lu,the disclosure of which is incorporated by reference, describes a systemfor integrating such an assembler array into a printer system withintegrated transfer to a final substrate. Still other electrodeconfigurations are possible.

Returning to FIG. 1, the electrodes 15A-15D are driven by amplifiers 18,which are in-turn controlled by a digital-to-analog converter 19. Thedigital-to-analog converter 19 is interfaced to at least one computingdevice 20 and under the control of the computing device 20, directs theelectrodes 15A-15D to generate specific voltages for specific periods oftime through the amplifiers 18 when commanded by the computing device 20interfaced to converter 19.

The computing device 20 is interfaced to a database 21 that storesparameters 22 of the system 10 that are relevant to the generation ofthe voltage and inducing movement of the micro-objects 12. Suchparameters 22 can include the dimensions and shape (such as spiral ornon-spiral shape) of the electrodes 15; the material of which theelectrodes 15, the film 14, the micro-objects, and the dielectric fluid11, are made; a thickness of the film 14; an intensity of the drag onthe micro-object 12 against the film 14; the dimension of themicro-object 12; and the voltage that the electrodes 15A-15D are capableof generating, though other parameters 22 are also possible.

Before exercising control over the electrodes 15A-15D, the computingdevice 20 executes a capacitance modeler 23 that uses the parameters 22to create a capacitance-based model 24 of the interaction between theelectric fields generated by the electrodes 15A-15D and themicro-objects 12. While the model 24 is described in relation to asingle micro-object 12, the model 24 can also be extended to covermultiple micro-objects 12. In a further embodiment, the modeler 23 canmodel a parameter of the interaction between the electrodes 15A-15D andthe micro-object 12 other than capacitance.

When the electrodes 15A-15D generate a voltage, dielectrophoretic andelectrophoretic forces are also generated by the electrodes 15A-15D. Theprojections of the dielectrophoretic and electrophoretic forces on amicro-object 12 in the x and y directions are denoted in the model 24 byF_(x)(x_(c), y_(c)) and F_(y) (x_(c), y_(c)), respectively, where(x_(c), y_(c)) represents the position of the center of themicro-object's 12 in two dimensions (the subscript of the coordinatesrefer to the center of the particle). A viscous drag force proportionalto the velocity of the movement of the micro-object 12 opposes themicro-object's 12 motion and is taken into account in the model 23. Dueto the negligible mass of the micro-object 12, acceleration is ignoredin creating the model 24. As experimentally shown, the micro-objects 12touch the surface of the film 14 when while being moved. Hence, themicro-object 12 remains at a constant height in the model 24; in theembodiment of the model 24 described below, the thickness of the film 14is 50 μm, y_(c) is set to be 50 μm, and the model 24 includes anadditional stiction force due to the micro-object 12 contact with thefilm 14 surface. The model 24 assumes the stiction force to beproportional to F_(y) (x_(c), y_(c)), when the F_(y) (x_(c), y_(c)),points downwards (and the micro-object 12 is pulled towards the contactsurface). Thus, the forces acting on the micro-object 12 that the model24 accounts for are summarized in the following motion equationscorresponding to the x-direction:μ{dot over (x)} _(c) =F _(x)(x _(c))−γ(t)F _(y)(x _(c)),  (1)

where

${\gamma(t)} = \left\{ \begin{matrix}{\gamma_{0}{{sign}\left( {\overset{.}{x}}_{c} \right)}{{F_{y}\left( x_{c} \right)}}} & {{F_{y}\left( x_{c} \right)} \leq 0} \\0 & {{F_{y}\left( x_{c} \right)} > 0}\end{matrix} \right.$

where μ is the viscous friction coefficient and {dot over (x)}_(c) isthe velocity of the micro-object in the x-direction. As the micro-object12 remains roughly at a constant height, the model 24 does not considervertical movement and thus no equation for the y direction is included.The intensity of the stiction force is controlled by the variable γ(t)whose definition reflects the additional surface drag only whenF_(y)(x_(c)) is negative (the micro-object 12 is pushed downwardstowards the film 14). The parameter γ₀, included as part of theparameters 22, models the intensity of the drag and is determinedexperimentally prior to using the system 10. γ₀ must be such that thetotal force can take positive values (assuming positive speed);otherwise, the micro-object 12 would never move.

The description of the model 24 references the electrodes 15A-15D of theunit 40, described above with reference to FIG. 2, however the model canalso be adapted to represent other kinds of electrode units withdifferent numbers of electrodes 15A-15D.

In the model 24, the forces F_(x)(x_(c)) and F_(y)(x_(c)) are expressedusing the potential energy of the micro-object 12. The potential energyis computed by representing the electrodes 15A-15D and the micro-objects12 as forming a capacitive-based electrical circuit. FIG. 4 is a diagram50 showing a representation of four electrodes 15A-15D and themicro-objects as forming a capacitance electrical circuit. The rationalefor such representation comes from the charge being induced on themicro-object 12 due to the dielectric properties of the dielectric fluid11. This phenomenon is similar to the behavior of a capacitor when apotential difference is applied to the capacitor. In addition, themicro-object 12 and the electrodes 15A-15D act as metal plates; hence,the capacitances of these capacitors are dependent on the micro-object12 position. As expected, the maximum capacitance values are attainedwhen the micro-object's 12 position maximizes overlap with theelectrodes. A small “leakage” capacitor C_(ε) is included to account forall the stray capacitance to the environment.

Let {V_(i)}_(i=1) ^(N) be the electric potentials to the ground at thefour electrodes 15A-15D. By representing the ground as the N+1 potential(V_(N+1)=0) and the leakage capacitor as the N+1 capacitor, thepotential energy of the micro-object 12 is given by

$\begin{matrix}{{U\left( {x_{c},y_{c}} \right)} = {{\frac{1}{2}{\sum\limits_{i = 1}^{N + 1}{{C_{i}\left( {{\overset{\_}{x}}_{i},{\overset{\_}{y}}_{i}} \right)}\left\lbrack {V_{i} - {V_{c}\left( {x_{c},y_{c}} \right)}} \right\rbrack}^{2}}} + {{qV}_{c}\left( {x_{c},y_{c}} \right)}}} & (2)\end{matrix}$

where q is the charge of the micro-object 12, x _(i)=x_(c)−x_(i), y_(i)=y_(c)−y_(i), with (x_(i), y_(i)) being the coordinates of anelectrode 15A-15D denoted as i. The first term reflects thedielectrophoretic effect while the second term describes theelectrophoresis. As empirically observed, the charge of the micro-object12 is negligible for the majority of experimental conditions. Hence, thedescription of the model 24 below focuses exclusively ondielectrophoresis as a modality to move the micro-object 12. At steadystate, the micro-object 12's potential at a given position can beexpressed as

$\begin{matrix}{{{V_{c}\left( {x_{c},y_{c}} \right)} = {\frac{1}{\Phi\left( {x_{c},y_{c}} \right)}{\sum\limits_{j = 1}^{N}{{C_{j}\left( {{\overset{\_}{x}}_{i},{\overset{\_}{y}}_{i}} \right)}V_{j}}}}},} & (3)\end{matrix}$

where Φ(x_(c), y_(c))=Σ_(j) C_(j)(x _(i), y _(i)). The N+1 term does notappear due to the zero potential. A closed form expression for thepotential energy follows:

$\begin{matrix}{{U\left( {x_{c},y_{c}} \right)} = {\frac{1}{2{\Phi\left( {x_{c},y_{c}} \right)}}{\sum\limits_{i > j}^{\;}{{C_{i}\left( {{\overset{\_}{x}}_{i},{\overset{\_}{y}}_{i}} \right)}{C_{j}\left( {{\overset{\_}{x}}_{i},{\overset{\_}{y}}_{i}} \right)}\left( {V_{i} - V_{j}} \right)^{2}}}}} & (4)\end{matrix}$

The dielectrophoretic forces acting on the micro-object 12 are computedby taking the gradient of the potential energy with respect to x and ydirections. Thus, the micro-object's 12 velocity along the x-directioncan be expressed as{dot over (x)} _(c) =V′Ξ(x _(c))V,  (5)

where V=[V_(1,2), . . . , V_(1,N), V_(2,3), . . . , V_(2,N), . . . ,V_(N−1,N)], with V_(i,j)=V_(i)−V_(j), and Ξ(x_(c)) a diagonal matrixwhose (i,j), (i,j) entry ((i,j) is seen as an index for a row or column)is given by

$\begin{matrix}{{\Xi\left( x_{c} \right)}_{{({i,j})},{({i,j})}} = {{{- \frac{1}{2{\mu\Phi}^{2}}}\frac{d\;\Phi}{dx}C_{i}C_{j}} + {\frac{1}{2{\mu\Phi}}\left( {{\frac{{dC}_{i}}{dx}C_{j}} + {\frac{{dC}_{j}}{dx}C_{i}}} \right)} + {\frac{\gamma}{2{\mu\Phi}^{2}}\frac{d\;\Phi}{dy}C_{i}C_{j}} - {\frac{\gamma}{2{\mu\Phi}}{\left( {{\frac{{dC}_{i}}{dy}C_{j}} + {\frac{{dC}_{j}}{dy}C_{i}}} \right).}}}} & (6)\end{matrix}$

Several additional simplifications can be performed by recalling thatthe spiral electrode pattern of the electrode unit 40 has four periodicelectrodes (V_(i,j)=V_(i)−V_(j), where V_(4k+i)=v_(i) and v_(i) is thepotential of line i∈{1, 2, 3, 4}), and by representing the potentialdifferences explicitly in terms of the four electrode lines potentialsv_(i).

The simplified micro-object 12 dynamics, quadratic in the electrodespotentials to the ground can be expressed as:{dot over (x)}=v′{tilde over (Ξ)}(x _(c))v,  (7)where v=[v₁, v₂, v₃, v₄] and {tilde over (Ξ)}(x_(c))=M′Ξ(x_(c))M. Thematrix M maps v to the vector of potential differences u=(v_(i,j),1≤i<j≤4). For example the first row of matrix M is [1, −1, 0, 0, 0, 0]so that the first entry of the vector u=Mv is v_(1,2)=v₁−v₂. To make themodel 24 useful for control design, the representations for thecapacitances between the electrodes and micro-object 12 are found interms of the micro-object 12 position.

The capacitance modeler 23 estimates the capacitances and their partialderivatives by performing simulations, wherein the simulation data isused to fit parametric models for the capacitances and their partialderivatives. In one embodiment, the modeler 23 can include acommercially available software, such as COMSOL Multiphysics®, producedby Comsol® AB Corporation of Stockholm, Sweden, and the capacitances andtheir partial derivatives by simulating a 2-dimensional electrostaticCOMSOL model. In the COMSOL® model, two metal plates with dimensionsgiven by the geometry of the micro-object 12 and electrodes 15A-15D aresurrounded by a dielectric with the same properties as the dielectricfluid 11. The quasi-static models are computed in the form ofelectromagnetic simulations using partial differential equations wherethe modeler 23 uses ground boundary (zero potential) as the boundarycondition of the models design. The capacitance matrix entries arecomputed from the charges that result on each conductor when an electricpotential is applied to one of them and the other is set to ground. Thismatrix maps the vector of potentials to the vector of charges. TheCOMSOL® simulations do reflect the field distortion when themicro-object 12 approaches the electrode. In a further embodiment, othersimulation software could be used instead of the COMSOL® software.

The capacitance modeler 23 can perform the simulations for a range ofmicro-object 12 positions. In one embodiment, the evaluated positionscan be x_(c)∈[−1 mm, 1 mm] and y_(c)∈{50 μm−δ, 50 μm, 50 μm+δ} withδ=1e−3 μm, though other positions can also be evaluated. Afterevaluating the capacitance at the different positions, the capacitancemodeler 23 can build a function included in the model 24 showing arelationship of between the position of the micro-object 12 and theelectrodes 15A-15D. FIG. 5 is a diagram depicting, by way of example,C(x,y), the capacitance between the micro-object 12 and the electrodes15A-15D as a function micro-object 12 horizontal position, with yconstant, where the simulated numerical values given were fitted on acontinuously differentiable function. The partial derivatives of C(x,y)with respect to x and y were determined by explicitly computing thepartial derivative with respect to x, and by numerical approximations,respectively

$\left. {\left( \frac{\partial{C\left( {x,y} \right)}}{\partial y} \right._{y = y_{c}^{*}} = \frac{{C\left( {x,{y_{c}^{*} + \delta}} \right)} - {C\left( {x,{y_{c}^{*} - \delta}} \right)}}{2\delta}} \right).$The diagram of FIG. 5 was generated using the simulations of the COMSOLMultiphysics® software, the fit of the simulated numerical calculationson the continuously differentiable function given by C(x,y_(c)*)=0.26031e−10[erf(−4522x+21.71)+erf(495.8x−3.432)+erf(−167.6x+0.6708)+erf(3121x+0.8186)+erf(−3119x+0.8176)+erf(167.4x−0.6707)+erf(11120x+1.86)+erf(−11120x+1.86)]for x∈[−2 mm, 2 mm] and zero otherwise.

To determine a reasonable approximation for μ, a term included in theequations (1) and (6) above, the modeler 23 uses the principle of“equivalent spherical diameter” and Stoke's law for the viscous drag ona sphere. Based on the dimensions of the micro-object 12, the radius ofa sphere that ensures the same volume is R=8.9470e−05 m. According toStoke's Law, the viscous drag for a sphere is F_(S)=6ηπRv=μv, with v thesphere velocity, and η=2.06 mPa·s, the coefficient of viscosity. Hence,μ≈3.4741e−06 N·s·m⁻¹. In the embodiment where the dielectric fluid isIsopar® M, given the density of Isopar® M (ρ=788 Kg·m⁻³) and themagnitude of the velocity, the Reynolds number is relatively small, andtherefore the modeler 23 uses Stoke's Law to model the viscous dragforce.

Thus, the model 24 takes into account the parameters 22 of the systemand accounts for the dependence of the capacitance between themicro-object 12 and the electrodes 15, which can be used, as describedbelow to determine a requisite amount of voltage that needs to begenerated by the electrodes to move the micro-object 12 towards adesired position. In a further embodiment, a different capacitance-basedmodel describing the relationship between the capacitance between theelectrodes 15A-15D and a micro-object 12 and the distance between theelectrodes 15A-15D and the micro-object 12 could be used to determinethe requisite amount of voltage.

Returning to FIG. 1, the computing device 20 further executes a positiontracker 25 that can track the position of the micro-objects 12. Thecomputing device 20 is interfaced, via a wired or wireless connection,to a high speed, high resolution camera 26 that performs the visualmonitoring of the electrodes 15A-15D and the micro-objects 12 on theelectrodes 15A-15D before and after movement of a micro-object 12 due tothe generation of the voltages by the electrodes 15A-15D. Raw data 27representing the monitored electrodes 15A-15D and the micro-objects 12that is captured by the camera 26 at particular points of time isprovided to the position tracker 25, and a frame grabber 28 included inthe position tracker 25 converts the raw data 27 into an image 29, andanalyzes the image 29, as further described below. In one embodiment,the images 29 can be grayscale images; in a further embodiment, theimages 29 can be color images. In one embodiment, the images can have a1696×1704 resolution at 543 frames per second, though othercharacteristics of the images 29 are also possible. The position tracker25 utilizes two properties of the electrodes 15A-15D and themicro-objects 12 for the analysis: that the electrodes 15A-15D arestatic and therefore may be considered the background of each of theimages 29 (the images 29 also designated f_(k) in the description below)and that the difference in micro-objects positions at consecutive timesteps is bounded by a small value ε, i.e., ∥p_(k+1), p_(k)∥<ε.

The position tracker 25 runs a background extraction algorithm toidentify background components 30 in an image 29 representing datacaptured before an initial movement of the micro-object 12. Classicalbackground subtraction algorithms such as those described by Stauffer etal., “Adaptive background mixture models for real-time tracking.” InComputer Vision and Pattern Recognition, 1999. IEEE Computer SocietyConference on., volume 2. IEEE, 1999, and Zoran Zivkovic, “Improvedadaptive gaussian mixture model for background subtraction,” in PatternRecognition, 2004. ICPR 2004. Proceedings of the 17th InternationalConference on, volume 2, pages 28-31. IEEE, 2004, the disclosures ofwhich are incorporated by reference, have several drawbacks. Suchalgorithms are based on classifying every pixel in each frame against acollection of weighted probability distributions where the weights aresorted in decreasing likelihood of modeling the background. Using thisapproach, given a pixel value in f_(k), the pixel is classified as abackground pixel if the pixel is close to a distribution (measured bythe Mahalanobis distance) that is likely to model a backgroundintensity. Updating the weights, means, and variances in the mixturemodel for each distribution at each pixel in f_(k) results in a dynamicmodel of the background so that when micro-objects become stationaryafter reaching their desired location, they are gradually absorbed intothe background. These algorithms largely succeed or fail based on thechoice of weights, thresholds, and distribution parameters. Thesealgorithm parameters may also stabilize too slowly given a poor initialmixture model. Despite stable parameters the background subtraction isunlikely to result in a clean segmentation of a micro-object 12 frombackground. More importantly, impurities in the dielectric fluid 11 dueto setup errors, atmospheric exposure, and dirt may be as large as a fewhundred microns and challenging to clean. Such impurities are alsosensitive to the applied electric field and will be perturbed, causingthem to be classified as part of the foreground.

Accordingly, the position tracker 25 uses background extractionalgorithm in combination with other processing techniques to monitor themovement of the micro-objects 12. In particular, before voltage isgenerated by the electrodes 15A-15D to move a particular micro-object12, the position tracker 25 runs at least one background subtractionalgorithm, such as one referenced in the Stauffer and Zincovic paperscited above, for several images 29 to stabilize parameters the mixturemodel for each distribution at each pixel in f_(k) results in a dynamicmodel of the background. Once the voltages are generated by theelectrodes 15A-15D and a micro-object 12 is moved due the generatedfield, the position tracker 25 determines the position of themicro-object 12, as also described below with reference to FIG. 12. Inparticular, the position tracker 25 identifies moving and non-movingparts of the images 29 (using comparison to the images 29 derived fromraw data 27 before and after the movement), with the non-movingcomponents being background components 30 and the moving componentsbeing foreground components 31. For the first few frames showing movingforeground objects, the position tracker 25 uses a morphological opening(which is an erosion followed by a dilation) of f_(k) with a disk whosediameter is the diameter of the maximal inscribed circle within themoved micro-object 12. In one embodiment, the morphological opening of ashape X with a disk D is the set (X! D)⊕D where ! and ⊕ represent theMinkowski difference and sum respectively, though other shapes of themorphological opening are possible. The application of the morphologicalopening is done to eliminate shapes and impurities that are smaller thanthe micro-object 12 size from the images 29. Groups of remainingconnected foreground components then correspond to micro-objects 12.

Once the position tracker 25 identifies the connected components, theposition tracker runs a template matching algorithm in a smallneighborhood around detected connected components. In one embodiment,the neighborhood can be 50×50 pixels, but other sizes of theneighborhood are also possible. The template used in the algorithm is animage of a micro-object 12, either the same micro-object 12 whoseposition tracked or a similar micro-object 12 (which can be stored inthe database 21 (not shown)), and the template matching proceeds toidentify the position of the template where there is maximal overlapwith the micro-object 12 in f_(k). The identification results in anaccurate estimation of the position 32 (which can be stored in thedatabase 21) corresponding to the center of the detected micro-object12, an example of which can be seen with reference to FIG. 6. FIG. 6 isdiagram 70 showing, by way of example, results of a template matchingalgorithm. As can be seen with reference to FIG. 6, the data provided bythe camera 26 can be analyzed to monitor motion of multiplemicro-objects 12 in relation to multiple electrode units 40, withpositions. The tracked positions are denoted by rectangles 41surrounding the micro-objects 12. While spiral electrode units 40 andhexagonal micro-objects 12 are shown, the template matching algorithmcan be used by the position tracker 25 for monitoring of a position ofmicro-objects 12 of other shapes in relation to other numbers ofelectrode units 40 and electrode units 40 of other shapes.

Returning to FIG. 1, once the positioner tracker 25 determines aposition of a micro-object 12 via performing the background extractionalgorithm, the morphological opening application, and the templatematching algorithm, the position tracker does not have to perform all ofthe same steps to identity the micro-object 12 in subsequent grayscaleimages 29 due to the next iteration of the generation of thedielectrophoretic field resulting in a small perturbation (centerdistances<ε) of in positions of the micro-objects. Accordingly, theposition tracker 25 defines a square window 33, which can be stored inthe database 21, of side of at least 2ε and runs the template matchingalgorithm described above within the window 33. In one embodiment, thesize of the window 33 can remain the same throughout determinations ofmultiple positions of the micro-object 12. In a further embodiment, thesize of the window 33 can be redefined after one or more positiondeterminations. Performing the template matching algorithm within thewindow 33, as opposed on an entire image 29, reduces the time necessaryto detect the center of the micro-object 12. Table 1 listsexperimentally found computation times for the template matchingalgorithm for various sizes of the windows.

TABLE 1 Window Size (pixels) Compute Time (ms) 960 × 1280 9.25 500 × 6500.426 300 × 300 0.035 100 × 100 0.025

The computing device 20 further executes a signal generator 35 thatgenerates a control scheme 36 for moving a micro-object from a knowninitial position 37 to a desired position 38. The control schemeincludes one or more control signals 39 that are applied to theelectrodes 15A-15D via the amplifiers 18 and the converter 19. Eachcontrol signal 39 is a command for the electrodes 15A-15D to generate aparticular voltage for a particular period of time. The control scheme36 further includes a predefined amount of time that must pass betweenthe applications of the control signals 39. Thus, the control signalsfor multiple electrodes 15A-15D in a unit 40 can be simultaneouslyapplied as a series of impulses, as further described below withreference to FIG. 9.

By having positions of the micro-object 12 tracked, the signal generator35 can generate one or more the signals 39 that would accomplish thedesired change in the positions of the micro-objects based on real-timefeedback, thus increasing the speed with which the object can bedelivered to a desired position. The scheme 36 is generatedprogressively, with the signals 39 being generated as the micro-object12 changes position. Thus, the signal generator implements a controlpolicy that is a based on a one-step ahead policy: for each position 32of the micro-object 12, the signal generator 35 generates a signal 39that brings the micro-object closer to a desired position 38. Followingthe generation of the signal 39, the signal generator 35 can apply thecontrol signals 39 by commanding the electrodes 15A-15D to generate thevoltages required by the signals 39 via the digital-to-analog converter19 and the amplifiers 18.

The generation of the signals 39 by the generator 35 utilizes thecapacitance-based model 24 that was created as described above. Inparticular, prior to applying a control signal, the signal generator 35models the dependence of the voltage generated (and consequently, of thegenerated forces) by one of the electrodes 15A-15D and a magnitude of achange in the position of a micro-object 12 due to generation of thevoltage by one electrode 15, creating a base function 34 describing thedependence. Once the movement of a micro-object needs to be accomplishedin real-time, the signal generator 35 leverages the base functions todesign the control scheme 36 for moving the micro-object from a startingposition 37 towards a desired position. In particular, the generator 35plugs a current position of the micro-object 12 into the base function34, outputting the change in the voltage that needs to be produced forone of the electrodes. As further described below, the requisite voltagefor the desired change in position for other electrodes, such as thethree remaining electrodes of unit 40, can be derived from the voltagefor the one electrode based using via translation operations and signchanges. Accordingly, due to not having to perform all of thecalculations in real-time and having the preexisting base function 34,the calculation of the control scheme 36 can be significantlyaccelerated.

As mentioned above, the signal generator 35 derives the base function 34prior to generating the control signals 39. The signal generator 25models the dependence of the magnitude of movement and the generatedvoltage as an optimization problem with both equality and inequalityconstraints and uses the capacitance-based model 24 in creating thefunction 34. The optimization problem is in the form of:

$\begin{matrix}{{\min\limits_{v_{i}}{{x_{ref} - {h\overset{.}{x}}}}^{2}}\mspace{14mu}{{{{subject}\mspace{14mu}{to}\text{:}\mspace{14mu}\overset{.}{x}} = {{{v^{\prime}{\overset{\sim}{\Xi}\left( {x_{c}(k)} \right)}v} - V_{\max}} \leq v_{i} \leq V_{\max}}},{i = 1},2,3,4}} & (8)\end{matrix}$

where x_(ref) denotes the desired position 37, h is a discrete step sizedescribing the distance that a micro-object 12 moves, v=[v₁, v₂, v₃,v₄]′, x_(c)(k) is the current position of a micro-object 12, and V_(max)is the maximum voltage that can be generated at an electrode 15A-15D. Inembodiments where a different number than four electrodes 15A-15D areinvolved in inducing micro-object 12 movement, v would be adapted toaccount for the different number of the electrodes 15A-15D. As shown bythe equation 8, the voltages that can move a micro-object towards adesired position is subject to two constraints: one constraint is thespeed with which the micro-object 12 can move, which is dependent on thecapacitance between the electrodes 15A-15D and the micro-object 12, asdescribed above; the second constraint is the magnitude of the voltagesthat can be generated by the electrodes, which is one of the parameters22. In one embodiment, V_(max) can be 400 Volts, though other values arealso possible.

In effect, the problem of equation (8) maximizes the x-direction of theinstantaneous velocity for a given position and under the voltageconstraints. The signal generator 35 solves the optimization problem formultiple values of desired position change and obtains the requisitevoltages (and consequently the requisite force) that need to begenerated by the electrodes 15A-15D to achieve the change. In oneembodiment, the signal generator 35 uses an optimization toolboxprovided by MATLAB® software developped by MathWorks, Inc. of Natick,Massachussets, to solve a sequence of optimization problems of the form(8) obtain the simulated values. An example of results of suchsimulations can be seen with reference to FIGS. 7A-7D.

FIGS. 7A-7D are diagrams showing, by way of example, the resultingelectrode voltage patterns as function of the micro-object position fromsolving a sequence of optimization problems of the equation (8). Theparameters 22 of the electrodes 15A-15D shown in FIG. 2 are used in thesimulation used to create the diagrams. The patterns are a set ofvoltages generated by each of the electrodes for a predefined period oftime and a change in the position of the micro-object resulting from thegeneration of the voltage by that electrode. The patterns were computedby solving equation (8) for a sequence of micro-object 12 positionsprior to attempting to move a micro-object 12 modeled by the equation inreal-time, thus mapping voltage values to magnitude of inducedmovements. The initial position of a micro-object 12 is shown to be −2mm and the desired position 37 is 2 mm. The sequence of electrodesuniformly distributed between −3 mm and 3 mm with a 200 μm spacingbetween their centers. The electrode voltages repeat every fourelectrodes. They are spatially periodic.

Based on the simulated values obtained as solutions to the equation (8),the signal generator 35 generates the base function 34 that describesthe dependence of a voltage generated at one electrode 15A-15D and themovement of a micro-object 12 due to the dielectrophoretic fieldgenerated by that electrode. FIG. 8 is a diagram 90 showing, by way ofexample, a base function describing the dependence of a voltagegenerated by one electrode 15A-15D and the movement of a micro-object 12induced by the voltage field generated by that electrode.

As mentioned above, there is a spatial periodicity between a position ofa micro-object 12 and the control input needed to move the micro-object12 towards a desired position 38. The signal generator 35 can use thebase function 34 and this spatial periodicity to construct the real-timecontrol scheme 36, which includes control signals 39 for generatingrequisite voltage. Thus, the signal generator 35 maps a position of amicro-object to the control input needed to move the micro-objecttowards the desired final position as fast as possible. The mappings arebased on two properties of the electrode voltages. First, there isperiodicity in the electrode voltages as a function of the micro-object12 position. Thus, the signal generator 35 needs to record the mappingbetween the position and the control input for one period only. Second,potentials of other electrodes 15A-15D in the unit 40 can be derivedfrom a potential one of the electrodes 15A-15D in the unit 40, viatranslation operations and sign change. These two properties allowreal-time implementation of the micro-object 12 position control.

The following example, described with reference to the electrodes15A-15D earlier whose voltage patterns were described above withreference to FIGS. 7A-7D, illustrates how the control signals formultiple electrode units can be generated from a voltage derived for oneof the electrodes via translation and sign changes. Let v₁(x) denote oneof the electrodes 15A-15D voltage as a function of position. Asmentioned above v₁ is spatially periodic, with a period of X=800 μm,that is v₁(x)=v₁(x+nX), for n∈Z. Moreover, the voltage pattern over oneperiod is given by the base function Ψ: [0,X]→[−400,400], also depictedin FIG. 8.

Hence, v₁ is generated according to the rulesv ₁(x)=v ₁(x+nX),v ₁(x)=Ψ(x),  (9)

with n∈Z and x∈[0, X). By analyzing the correlation between v₁ and therest of the voltages for the remaining 3 electrodes, the last threeelectrodes voltages (denoted v₂, v₃, and v₄) can be expressed asv ₂(x)=−v ₁(x+δ),v ₃(x)=v ₁(x+2δ),v ₄(x)=−v ₁(x−δ)  (10)

where δ=200 μm in one embodiment. The value of δ represents the distancebetween centers of electrodes 15A-15D Therefore, to move themicro-object 12 in the positive direction a position dependent controlinput can be generated using only the base function 34, denoted as Ψ(x),and the rules of equations (9)-(10). Similar rules in terms of Ψ(x) canbe determined to move the micro-object 12 in the negative direction.

Generally, a significant amount of time may be necessary to obtain anaccurate position estimate for a micro-object 12, and the micro-object12 must remain stationary during the estimation, thus slowing down themicro-object assembly. To cope with these challenges, the signalgenerator 35 uses an impulse control policy where a short controlimpulse of a predefined length based on a current known position isapplied, followed by a waiting period to allow for the next positionestimate to stabilize. More formally, let h_(s) be the time period atwhich the micro-object position is updated, and let V_(i)(t) be thecontrol signals applied to the electrodes, with i=1, 2, 3, 4. If adifferent number of electrodes is used for movement of the micro-object,i would change accordingly. FIG. 9 is a diagram showing, by way ofexample, an impulse-based real-time control scheme 36. The impulse-basedreal time control scheme, pictorially depicted in FIG. 9, is defined bysmall

$\begin{matrix}{{V_{i}(t)} = \left\{ \begin{matrix}{v_{i}\left( x_{k} \right)} & {t \in \left\lbrack {{kh}_{s},{{kh}_{s} + h_{c}}} \right)} \\0 & {t \in t \in \left\lbrack {{{kh}_{s} + h_{c}},{\left( {k + 1} \right)h_{s}}} \right)}\end{matrix} \right.} & (11)\end{matrix}$where x_(k) is the position of the micro-object at time kh_(s) and h_(c)is the duration of the control impulse. After the application of thecontrol impulse, the micro-object 12 can be assumed stationary due toits negligible mass and the viscous drag. Hence, the trackingstabilization time is given by τ=h_(s)−h_(c). In a further embodiment,the signal generator 35 generates signals 38 to be applied asconsecutive impulses based on the current position of the micro-object.Still other ways to obtain the voltages necessary for inducing a desiredmovement of the micro-particle are possible.

As described above, the voltages generated by multiple electrodes canaffect a single micro-object that is proximate to those electrodes 15,and a control scheme 36 can account for the voltages generated by themultiple electrodes. FIGS. 10A-10D are diagrams showing, by way ofexample, a control scheme 36 for using the control unit 40 of FIG. 2 tomove a micro-object 1000 μm. The control scheme 36 in FIGS. 10A-10D isapplied to a control unit 40 that includes four electrodes 15, with themicro-object being a silicon chiplet of 300 μm×200 μm×50 μm dimensions.When implemented, the control scheme results in stepwise movements ofthe micro-object, as shown in FIG. 11. FIG. 11 is a diagram 100 showing,by way of example, the movement of a micro-object 12 as a result ofapplication of control scheme of FIG. 10. The position of themicro-object was tracked every 10 miliseconds. The dashed linesrepresent the estimated positions of the micro-object 12, while thesolid dots show the samples of the micro-object positions 12.

While the at least one computing device 20 is shown as a server, othertypes of computer devices are possible, such desktop computers, laptopcomputers, mobile devices such as tablets and smartphones, though stillother types of computing devices 20 are possible. The computing device20 can include one or more modules for carrying out the embodimentsdisclosed herein. The modules can be implemented as a computer programor procedure written as source code in a conventional programminglanguage and is presented for execution by the central processing unitor a graphics processing unit (GPU) as object or byte code.Alternatively, the modules could also be implemented in hardware, eitheras integrated circuitry or burned into read-only memory components, andeach of the servers can act as a specialized computer. For instance,when the modules are implemented as hardware, that particular hardwareis specialized to perform the computations and communication describedabove and other computers cannot be used. Additionally, when the modulesare burned into read-only memory components, the computer storing theread-only memory becomes specialized to perform the operations describedabove that other computers cannot. The various implementations of thesource code and object and byte codes can be held on a computer-readablestorage medium, such as a floppy disk, hard drive, digital video disk(DVD), random access memory (RAM), read-only memory (ROM) and similarstorage mediums. Other types of modules and module functions arepossible, as well as other physical hardware components. For example,the computing device 20 can include other components found inprogrammable computing devices, such as input/output ports, networkinterfaces, and non-volatile storage, although other components arepossible. In the embodiment the computing device 20 is a server, theserver can also be cloud-based or be dedicated servers. The computingdevice 20 can be connected to the converter 19 and the camera 26 eitherdirectly or via a network, including an Internetwork such as theInternet.

In a further embodiment, the computing device could further control theassembly of multiple micro-objects at the same time by implementing thealgorithms described above and below in parallel using the GPU.

By deriving the capacitance-based model and the base function prior tocontrolling a micro-assembly, the speed with which the assembly can beperformed in real-time can be greatly accelated. FIG. 12 is a flowdiagram showing a method 110 for scalable real-time micro-objectposition control with the aid of a digital computer. The method 110 canbe implemented using the system 10 of FIG. 1, though otherimplementations are also possible. Parameters for the micro-objectspositioning assembly are obtained (step 111). A capacitance-based modelof the interaction between the electric fields generated by one or moreelectrodes and a micro-object is created as described above withreference to FIG. 1 using the parameters (step 112). In a furtherembodiment, a parameter of the interaction between an electrode 15A-15Dand the micro-object 12 can be modeled and used during subsequentcalculations.

A base function for generating voltage patterns is derived using thecapacitance-based model, as further described above with reference toFIG. 1 and below with reference to FIG. 13 below (step 113). Controlsignals for moving the micro-object towards a desired position aregenerated using the base function and a starting position (step 114). Asdescribed above with reference to FIG. 1, a voltage that needs to beapplied for one of the electrodes is first generated by entering thecurrent position of the micro-object (which is the starting position ofthe micro-object during a first iteration of step 114 during anexecution of the method 110) into the base function and obtaining therequisite voltage as the output of the base function. The voltages forone or more additional electrodes based on the voltages for the oneelectrode through translation operations and sign changes as describedabove with reference to FIG. 1. The generated control signals areapplied by commanding implementation of the calculated voltages for aperiod of time defined by the control signals on the electrodes (step115). If more than one iteration of step 114 are performed, the startingposition of the micro-object is updated to reflect the trackingdescribed below in step 116.

A position of the micro-object is tracked (step 116), as furtherdescribed above with reference to FIG. 1 and below with reference toFIG. 14. If following the generation of the voltage by one or moreelectrodes the micro-object has reached the desired position (step 117),the method 110 ends. If the micro-object has not reached the desiredposition (step 117), the method 110 returns to step 114, where a furthercontrol signal is generated to allow for another movement of themicro-object 12.

Creating the capacitance-based model allows to account for the potentialenergy of the micro-object. FIG. 13 is a flow diagram describing aroutine 120 for generating the capacitance-based model for use in themethod 110 of FIG. 12, in accordance with one embodiment. A capacitancebetween the micro-object 12 and each of the electrodes involved inmoving the micro-object is simulated at a plurality of distances asdescribed above with reference to FIG. 1 (step 121). A function includedin the model 24 showing a relationship of between the position of themicro-object 12 and the electrodes 15A-15D is derived based on theestimated results (step 122), ending the routine 120.

Deriving the base function that is used for determining the amount ofvoltage that needs to be generated by the electrodes in advance ofhaving to apply the control signals allows to significantly speed-up thecalculations necessary for the signal generation. FIG. 14 is a flowdiagram showing a routine 130 for deriving a base function for controlsignal generation for use in the method 110 of FIG. 12 in accordancewith one embodiment. Simulations are performed to obtain a plurality ofvalues for voltages generated by an electrode for a predefined period oftime corresponding to a certain amount of movements caused by thegeneration of the particular voltage values, as described above withreference to FIG. 1 (step 131). A base function that describes therelationship between the voltage generated at an electrode and theamount of movement of a micro-object due to the generated voltage isderived based on the plurality of values obtained due to the simulation,as described in detail above with reference to FIG. 1 (step 132), endingthe routine 130.

Monitoring the position of a micro-object 12 in between applications ofcontrol signals allows to obtain feedback to the control signals andimplement corrections to the control scheme, thus improving thepreciseness and the speed with which a micro-object is placed into adesired position. FIG. 15 is a flow diagram showing a routine 140 fortracking a position of a micro-object for use in the method 110 of FIG.12 in accordance with one embodiment. Raw data from monitoring of amicro-object 12 in relation to one or more electrodes 15A-15D isreceived from a high-speed, high-resolution camera (step 141). Duringthe first iteration of step 141, the raw data includes visual datacaptured before and after an application of a control signal to theelectrodes for a first time during an implementation of the method 110.The raw data is processed by a frame grabber and converted into at leastone image (step 142). If the routine 140 is performed for the first timeduring the implementation of the method 140 (step 143), at least onebackground subtraction algorithm is performed on several images thatwere derived from the raw data captured before the first application ofthe control signal to stabilize parameters in the mixture model that canbe used to differentiate background elements from foreground elements(step 144). Images derived from raw data captured after an applicationof a control signal are analyzed and all moving objects, when comparedto the digital images based on the raw data captured before theimplementation of a control signal, are identified as foregroundcomponents, with non-moving objects being identified as backgroundcomponents (step 145). A morphological opening is applied to theidentified foreground component, as described above with reference toFIG. 1, to foreground components that are smaller than the size of themicro-object 12 (step 146). A template matching algorithm that uses animage of the micro-object as comparison is ran around the identifiedforeground components that were identified to identify the micro-object12 in the image and thus determine the position of the micro-object 12(and additional micro-objects 12 if present) (step 147). A window isdefined around the identified micro-objects for use in subsequenttracking of the micro-object position 12, as described in detail abovewith reference to FIG. 1, ending the routine 140.

If the routine 140 is not performed for the first time during aparticular execution of the method 110, a template matching algorithmusing the image of the micro-object as a template is performed withinthe window previously constructed in step 147 (step 148), identifyingthe current position of the micro-object and ending the routine.Optionally, the window may be redefined after one or more additionaliterations of step 148 (not shown).

While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope of theinvention.

What is claimed is:
 1. A system for scalable real-time micro-objectposition control with the aid of a digital computer, comprising: atleast one processor configured to execute computer-executable code andfurther configured to: obtain one or more parameters of a system forpositioning of at least one micro-object, the system comprising aplurality of electrodes, the electrodes configured to induce a movementof the micro-object when the micro-object is suspended in a fluidproximate to the electrodes upon a generation of one or more forces bythe electrodes; model a capacitance of an interaction between each ofthe electrodes and the micro-object as a function of a distance betweenthat electrode and the micro-object using the system parameters; obtaina position of the micro-object within the fluid; obtain using themodeled capacitance a further function describing a relationship betweenthe forces generated by one of the electrodes for a predefined period oftime and an extent of the movement induced by the forces generated bythe one electrode, comprising: map a plurality of values for the one ormore forces generated by the one electrode to the extent of the movementof the micro-object induced by the generation of each of the voltagevalues; and derive the further function from the mappings; and generatea control signal for each of the electrodes, the control signalcomprising a command to generate one or more of the forces for thepredefined period of time by that electrode, using the obtainedposition, the further function, and a desired location of themicro-object.
 2. A system according to claim 1, wherein the electrodesare spiral electrodes and are comprised in an electrode unit.
 3. Asystem according to claim 1, wherein the electrodes are periodicallyspaced.
 4. A system according to claim 1, the at least one processorfurther configured to: perform a plurality of simulations of theinteraction parameters at a plurality of values of the distance, whereinthe modeling is done based on the simulations.
 5. A system according toclaim 1, the at least one processor further configured to: apply thecontrol signals to the electrodes, wherein the electrodes generate theone or more forces commanded by the control signals upon the applicationand induce the movement of the micro-object; identify a further positionof the micro-object following the application of the control signals tothe electrodes; and generate further control signals for the electrodesusing the interaction parameter, the further position of themicro-object, and the desired position of the micro-object following theapplication.
 6. A system according to claim 5, the at least oneprocessor further configured to: receive raw data captured by ahigh-speed, high-resolution camera monitoring the micro-object, the rawdata having been captured before and after the application of thecontrol signals; convert the raw data into a plurality of images using aframe grabber; perform background extraction on the images convertedfrom the raw data captured before the application of the controlsignals; identify foreground components in at least one of the imagesconverted from the raw data captured after the application of the signalusing a result of the background extraction; identify the foregroundcomponents whose size is greater than or equals to a predefined size;and perform a template matching algorithm in a neighborhood associatedwith the foreground components to identify the position of themicro-object.
 7. A system according to claim 5, the at least oneprocessor further configured to: apply a morphological opening to theforeground components to identify the foreground components whose sizeis greater than or equal to the predefined size.
 8. A system accordingto claim 5, the at least one processor further configured to: define awindow around the identified position, wherein the template matchingalgorithm is run in a neighborhood defined by the window to determine asubsequent position of the micro-object.
 9. A system according to claim1, the at least one processor further configured to: perform a pluralityof simulations to obtain the mappings.
 10. A method for scalablereal-time micro-object position control with the aid of a digitalcomputer, comprising the steps of: obtaining one or more parameters of asystem for positioning of at least one micro-object, the systemcomprising a plurality of electrodes, the electrodes configured toinduce a movement of the micro-object when the micro-object is suspendedin a fluid proximate to the electrodes upon a generation of one or moreforces by the electrodes; modeling a capacitance of an interactionbetween each of the electrodes and the micro-object as a function of adistance between a location of that electrode and a location of themicro-object using the system parameters; obtaining a position of themicro-object within the fluid; obtaining using the modeled capacitance afurther function describing a relationship between the forces generatedat one of the electrodes for a predefined period of time and an extentof the movement induced by the forces generated by the one electrode,comprising: mapping a plurality of values for the one or more forcesgenerated by the one electrode to the extent of the movement of themicro-object induced by the generation of each of the voltage values;and deriving the further function from the mappings; and generating acontrol signal for each of the electrodes, the control signal comprisinga command to generate one or more of the forces for the predefinedperiod of time by that electrode, using the obtained position, thefurther function, and a desired location of the micro-object, whereinthe steps are performed by a computer.
 11. A method according to claim10, wherein the electrodes are spiral electrodes and are comprised in anelectrode unit.
 12. A method according to claim 10, wherein theelectrodes are periodically spaced.
 13. A method according to claim 10,further comprising: performing a plurality of simulations of theinteraction parameters at a plurality of values of the distance, whereinthe modeling is done based on the simulations.
 14. A method according toclaim 10, further comprising: applying the control signals to theelectrodes, wherein the electrodes generate the one or more forcescommanded by the control signals upon the application and induce themovement of the micro-object; identifying a further position of themicro-object following the application of the control signals to theelectrodes; and generating further control signals for the electrodesusing the interaction parameter, the further position of themicro-object, and the desired position of the micro-object following theapplication.
 15. A method according to claim 14, further comprising:receiving raw data captured by a high-speed, high-resolution cameramonitoring the micro-object, the raw data having been captured beforeand after the application of the control signals; converting the rawdata into a plurality of images using a frame grabber; performingbackground extraction on the images converted from the raw data capturedbefore the application of the control signals; identifying foregroundcomponents in at least one of the images converted from the raw datacaptured after the application of the signal using a result of thebackground extraction; identifying the foreground components whose sizeis greater than or equals to a predefined size; and performing atemplate matching algorithm in a neighborhood associated with theforeground components to identify the position of the micro-object. 16.A method according to claim 15, further comprising: applying amorphological opening to the foreground components to identify theforeground components whose size is greater than or equal to thepredefined size.
 17. A method according to claim 15, further comprising:defining a window around the identified position, wherein the templatematching algorithm is run in a neighborhood defined by the window todetermine a subsequent position of the micro-object.
 18. A methodaccording to claim 10, comprising: performing a plurality of simulationsto obtain the mappings.